{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10422,"status":"ok","timestamp":1721265691271,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"rTzMWVRrdUCn","outputId":"64338ea6-4e0e-48f9-9b63-573735729e33"},"outputs":[],"source":["pip install einops"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":75115,"status":"ok","timestamp":1721265766384,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"9CqRFchzdkqh","outputId":"edf394e3-3063-4a40-8dc6-6eba10c6e235"},"outputs":[],"source":["pip install timm"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":8531,"status":"ok","timestamp":1721265774913,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"EkAhKs3Edluh"},"outputs":[{"ename":"ModuleNotFoundError","evalue":"No module named 'sklearn'","output_type":"error","traceback":["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m","\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","Cell \u001b[1;32mIn[1], line 8\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmanifold\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TSNE\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m io \u001b[38;5;28;01mas\u001b[39;00m sio\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n","\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"]}],"source":["import numpy as np\n","from preclassify import dicomp, hcluster\n","import cv2\n","import torch\n","import torch.nn as nn\n","import time\n","import numpy as np\n","from sklearn.manifold import TSNE\n","from scipy import io as sio\n","import matplotlib.pyplot as plt\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from einops.layers.torch import Rearrange\n","from torch.optim.lr_scheduler import StepLR\n","from torch.utils.data import DataLoader\n","from timm.models.layers import trunc_normal_\n","from torch.autograd import Function\n","from torch_wavelets import DWT_2D, IDWT_2D\n","import pywt\n","from skimage import io, measure\n","import random\n","import math\n","from tqdm.notebook import tqdm\n","import os\n","from PIL import Image"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":570,"status":"ok","timestamp":1721265878225,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"5lwfNoETdpiJ"},"outputs":[],"source":["def pair(t):\n","    return t if isinstance(t, tuple) else (t, t)\n","\n","\n","seed = 3407\n","\n","def seed_everything(seed):\n","    random.seed(seed)\n","    os.environ['PYTHONHASHSEED'] = str(seed)\n","    np.random.seed(seed)\n","    torch.manual_seed(seed)\n","    torch.cuda.manual_seed(seed)\n","    torch.cuda.manual_seed_all(seed)\n","    torch.backends.cudnn.deterministic = True\n","\n","\n","seed_everything(seed)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1721265880505,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"KXX-qZhHdtJI"},"outputs":[],"source":["def image_normalize(data):\n","    import math\n","    _mean = np.mean(data)\n","    _std = np.std(data)\n","    npixel = np.size(data) * 1.0\n","    min_stddev = 1.0 / math.sqrt(npixel)\n","    return (data - _mean) / max(_std, min_stddev)\n","\n","\n","def image_padding(data, r):\n","    if len(data.shape) == 3:\n","        data_new = np.lib.pad(data, ((r, r), (r, r), (0, 0)), 'constant', constant_values=0)\n","        return data_new\n","    if len(data.shape) == 2:\n","        data_new = np.lib.pad(data, r, 'constant', constant_values=0)\n","        return data_new\n","\n","\n","def arr(length):\n","    arr = np.arange(length - 1)\n","    # print(arr)\n","    random.shuffle(arr)\n","    # print(arr)\n","    return arr\n","\n","\n","def createTrainingCubes(X, y, patch_size):\n","    margin = int((patch_size - 1) / 2) + 1\n","    zeroPaddedX = image_padding(X, margin)\n","    ele_num1 = np.sum(y == 1)\n","    ele_num2 = np.sum(y == 2)\n","    patchesData_1 = np.zeros((ele_num1, patch_size, patch_size, X.shape[2]))\n","    patchesLabels_1 = np.zeros(ele_num1)\n","    patchesData_2 = np.zeros((ele_num2, patch_size, patch_size, X.shape[2]))\n","    patchesLabels_2 = np.zeros(ele_num2)\n","\n","    patchIndex_1 = 0\n","    patchIndex_2 = 0\n","    for r in range(margin, zeroPaddedX.shape[0] - margin):\n","        for c in range(margin, zeroPaddedX.shape[1] - margin):\n","            # remove uncertainty pixels\n","            if y[r - margin, c - margin] == 1:\n","                patch_1 = zeroPaddedX[r - margin:r + margin, c - margin:c + margin]\n","                patchesData_1[patchIndex_1, :, :, :] = patch_1\n","                patchesLabels_1[patchIndex_1] = y[r - margin, c - margin]\n","                patchIndex_1 = patchIndex_1 + 1\n","            elif y[r - margin, c - margin] == 2:\n","                patch_2 = zeroPaddedX[r - margin:r + margin, c - margin:c + margin]\n","                patchesData_2[patchIndex_2, :, :, :] = patch_2\n","                patchesLabels_2[patchIndex_2] = y[r - margin, c - margin]\n","                patchIndex_2 = patchIndex_2 + 1\n","    patchesLabels_1 = patchesLabels_1 - 1\n","    patchesLabels_2 = patchesLabels_2 - 1\n","\n","    arr_1 = arr(len(patchesData_1))\n","    arr_2 = arr(len(patchesData_2))\n","    train_len = 10000  \n","    pdata = np.zeros((train_len, patch_size, patch_size, X.shape[2]))\n","    plabels = np.zeros(train_len)\n","    for i in range(7000):\n","        pdata[i, :, :, :] = patchesData_1[arr_1[i], :, :, :]\n","        plabels[i] = patchesLabels_1[arr_1[i]]\n","    for j in range(7000, train_len):\n","        pdata[j, :, :, :] = patchesData_2[arr_2[j - 7000], :, :, :]\n","        plabels[j] = patchesLabels_2[arr_2[j - 7000]]\n","\n","    return pdata, plabels\n","\n","\n","def createTestingCubes(X, patch_size):\n","    margin = int((patch_size - 1) / 2) + 1\n","    zeroPaddedX = image_padding(X, margin)\n","    patchesData = np.zeros((X.shape[0] * X.shape[1], patch_size, patch_size, X.shape[2]))\n","    patchIndex = 0\n","    for r in range(margin, zeroPaddedX.shape[0] - margin):\n","        for c in range(margin, zeroPaddedX.shape[1] - margin):\n","            patch = zeroPaddedX[r - margin:r + margin, c - margin:c + margin]\n","            patchesData[patchIndex, :, :, :] = patch\n","            patchIndex = patchIndex + 1\n","    return patchesData\n","\n","\n","#  Inputs:  gtImg  = ground truth image\n","#           tstImg = change map\n","#  Outputs: FA  = False alarms\n","#           MA  = Missed alarms\n","#           OE  = Overall error\n","#           PCC = Overall accuracy\n","def evaluate(gtImg, tstImg):\n","    gtImg[np.where(gtImg > 128)] = 255\n","    gtImg[np.where(gtImg < 128)] = 0\n","    tstImg[np.where(tstImg > 128)] = 255\n","    tstImg[np.where(tstImg < 128)] = 0\n","    [ylen, xlen] = gtImg.shape\n","    FA = 0\n","    MA = 0\n","    label_0 = np.sum(gtImg == 0)\n","    label_1 = np.sum(gtImg == 255)\n","    print(label_0)\n","    print(label_1)\n","\n","    for j in range(0, ylen):\n","        for i in range(0, xlen):\n","            if gtImg[j, i] == 0 and tstImg[j, i] != 0:\n","                FA = FA + 1\n","            if gtImg[j, i] != 0 and tstImg[j, i] == 0:\n","                MA = MA + 1\n","\n","    OE = FA + MA\n","    PCC = 1 - OE / (ylen * xlen)\n","    PRE = ((label_1 + FA - MA) * label_1 + (label_0 + MA - FA) * label_0) / ((ylen * xlen) * (ylen * xlen))\n","    KC = (PCC - PRE) / (1 - PRE)\n","    print(' Change detection results ==>')\n","    print(' ... ... FP:  ', FA)\n","    print(' ... ... FN:  ', MA)\n","    print(' ... ... OE:  ', OE)\n","    print(' ... ... PCC: ', format(PCC * 100, '.2f'))\n","    print(' ... ... KC: ', format(KC * 100, '.2f'))\n","\n","\n","def postprocess1(res):\n","    res_new = res\n","    res = measure.label(res, connectivity=2)\n","    # print(res)\n","    num = res.max()\n","    # print(num)\n","    for i in range(1, num + 1):\n","        idy, idx = np.where(res == i)\n","        if len(idy) <= 15:\n","            res_new[idy, idx] = 0.5\n","    return res_new\n","\n","\n","def postprocess(res):\n","    res_new = res\n","    res = measure.label(res, connectivity=2)\n","    # print(res)\n","    num = res.max()\n","    # print(num)\n","    for i in range(1, num + 1):\n","        idy, idx = np.where(res == i)\n","        if len(idy) <= 20:\n","            res_new[idy, idx] = 0\n","    return res_new"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27393,"status":"ok","timestamp":1721268819830,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"t7JnDkesdxSo","outputId":"f2a623c2-800d-47ea-a6c7-6f981b0db5e7"},"outputs":[],"source":["\n","im1_path = '/content/Sulzberger2_1.bmp'\n","im2_path = '/content/Sulzberger2_2.bmp'\n","imgt_path = '/content/Sulzberger2_gt.bmp'\n","\n","\n","patch_size = 8\n","\n","\n","if im1_path == '/content/Yellow_River_1.bmp':\n","  im1 = io.imread(im1_path).astype(np.float32)\n","  im2 = io.imread(im2_path).astype(np.float32)\n","  im_gt = io.imread(imgt_path).astype(np.float32)\n","else:\n","  im1 = io.imread(im1_path)[:, :, 0].astype(np.float32)\n","  im2 = io.imread(im2_path)[:, :, 0].astype(np.float32)\n","  im_gt = io.imread(imgt_path)[:, :, 0].astype(np.float32)\n","\n","\n","\n","im_di = dicomp(im1, im2)\n","ylen, xlen = im_di.shape\n","pix_vec = im_di.reshape([ylen * xlen, 1])\n","\n","preclassify_lab = hcluster(pix_vec, im_di)\n","print('... ... hiearchical clustering finished !!!')\n","\n","mdata = np.zeros([im1.shape[0], im1.shape[1], 3], dtype=np.float32)\n","mdata[:, :, 0] = im1\n","mdata[:, :, 1] = im2\n","mdata[:, :, 2] = im_di\n","mlabel = preclassify_lab\n","\n","x_train, y_train = createTrainingCubes(mdata, mlabel, patch_size)\n","x_train = x_train.transpose(0, 3, 1, 2)\n","# print(y_train.shape) #[10000]\n","x_test = createTestingCubes(mdata, patch_size)\n","x_test = x_test.transpose(0, 3, 1, 2)\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","\n","class TrainDS(torch.utils.data.Dataset):\n","    def __init__(self):\n","        self.len = x_train.shape[0]\n","        self.x_data = torch.FloatTensor(x_train)\n","        self.y_data = torch.LongTensor(y_train)\n","\n","    def __getitem__(self, index):\n","        return self.x_data[index], self.y_data[index]\n","\n","    def __len__(self):\n","        return self.len\n","\n","\n","trainset = TrainDS()\n","\n","train_loader = torch.utils.data.DataLoader(dataset=trainset, batch_size=128, shuffle=True, num_workers=2)\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":590,"status":"ok","timestamp":1721265939178,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"eIYcutYBejcI"},"outputs":[],"source":["class WaveAttention(nn.Module):\n","\n","    def __init__(self, sr_ratio, dim, heads, dropout):\n","        super(WaveAttention, self).__init__()\n","        self.heads = heads\n","        self.dim = dim\n","        head_dim = dim // heads\n","        self.head_dim = dim // heads\n","        self.sr_ratio = sr_ratio\n","        self.scale = head_dim ** -0.5\n","        self.dwt = DWT_2D(wave=\"haar\")\n","        self.idwt = IDWT_2D(wave=\"haar\")\n","\n","        self.reduce = nn.Sequential(\n","            nn.Conv2d(dim, dim // 4, kernel_size=1, padding=0, stride=1),\n","            nn.BatchNorm2d(dim // 4),\n","            nn.ReLU(inplace=True)\n","        )\n","        self.filter = nn.Sequential(\n","            nn.Conv2d(dim, dim, kernel_size=3, padding=1, stride=1, groups=1),\n","            nn.BatchNorm2d(dim),\n","            nn.ReLU(inplace=True)\n","        )\n","        self.q = nn.Linear(dim, dim)\n","        self.kv = nn.Sequential(\n","            nn.LayerNorm(dim),\n","            nn.Linear(dim, dim * 2)\n","        )\n","\n","        self.kv_embed = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) if sr_ratio > 1 else nn.Identity()\n","\n","        self.proj = nn.Sequential(\n","            nn.Linear(dim + dim // 4, dim),\n","            nn.Dropout(dropout)\n","        )\n","\n","        self.apply(self._init_weights)\n","\n","\n","    def forward(self, x, H, W):\n","        B, N, C = x.shape\n","        q = self.q(x).reshape(B, N, self.heads, self.head_dim).permute(0, 2, 1, 3)\n","        # checkShape(\"q\", q)\n","        x = x.view(B, H, W, C).permute(0, 3, 1, 2)\n","        x = self.reduce(x)\n","        x = torch.tensor(x, device=device).type(torch.float16)\n","        x_dwt = self.dwt(x)\n","        x_dwt = x_dwt.float()\n","        x_dwt = self.filter(x_dwt)\n","\n","        x_dwt = x_dwt.half()\n","        x_idwt = self.idwt(x_dwt)\n","        x_idwt = x_idwt.view(B, -1, x_idwt.size(-2) * x_idwt.size(-1)).transpose(1, 2)\n","\n","        x_dwt = x_dwt.float()\n","        kv = self.kv_embed(x_dwt).reshape(B, C, -1).permute(0, 2, 1)\n","        kv = self.kv(kv).reshape(B, -1, 2, self.heads, self.head_dim).permute(2, 0, 3, 1, 4)\n","        k = kv[0]\n","        v = kv[1]\n","        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale\n","        attn = attn.softmax(dim=-1)\n","        x = torch.matmul(attn, v).transpose(1, 2).reshape(B, N, C)  # N ->H*W\n","        x = self.proj(torch.cat([x, x_idwt], dim=-1))\n","        return x\n","\n","    def _init_weights(self, m):\n","        if isinstance(m, nn.Linear):\n","            trunc_normal_(m.weight, std=.02)\n","            if isinstance(m, nn.Linear) and m.bias is not None:\n","                nn.init.constant_(m.bias, 0)\n","        elif isinstance(m, nn.LayerNorm):\n","            nn.init.constant_(m.bias, 0)\n","            nn.init.constant_(m.weight, 1.0)\n","        elif isinstance(m, nn.Conv2d):\n","            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n","            fan_out //= m.groups\n","            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))\n","            if m.bias is not None:\n","                m.bias.data.zero_()\n","\n","class WSM(nn.Module):\n","    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool='cls', channels=3,\n","                 dim_head=64, dropout=0., emb_dropout=0.):\n","        super().__init__()\n","        image_height, image_width = pair(image_size)\n","        patch_height, patch_width = pair(patch_size)\n","        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be ' \\\n","                                                                                    'divisible by the patch size. '\n","        num_patches = (image_height // patch_height) * (image_width // patch_width)\n","        patch_dim = channels * patch_height * patch_width\n","        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'\n","        self.to_patch_embedding = nn.Sequential(\n","            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_height, p2=patch_width),\n","            nn.Linear(patch_dim, dim),\n","        )\n","        self.transformer = WaveAttention(sr_ratio=2, dim=dim, heads=heads, dropout=dropout)\n","        self.reshape = Rearrange('b (h w) (p1 p2  c) -> b (h p1) (w p2) c', p1=patch_height, p2=patch_width,\n","                                 h=image_height // patch_height)\n","\n","    def forward(self, img):\n","        x = self.to_patch_embedding(img)\n","        x = self.transformer(x, H=4, W=4)\n","        x = self.reshape(x)\n","        return x\n","\n","\n","wsm = WSM(image_size=8, patch_size=2, num_classes=2, dim=12, depth=6, heads=4, mlp_dim=32).to(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":566,"status":"ok","timestamp":1721265942820,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"rxQBIBqBezQ5"},"outputs":[],"source":["class BiAttn(nn.Module):\n","    def __init__(self, in_channels, act_ratio=0.5, act_fn=nn.GELU, gate_fn=nn.Sigmoid):\n","        super().__init__()\n","        reduce_channels = int(in_channels * act_ratio)\n","        self.norm = nn.LayerNorm(in_channels)\n","        self.global_reduce = nn.Linear(in_channels, reduce_channels)\n","        self.local_reduce = nn.Linear(in_channels, reduce_channels)\n","        self.act_fn = act_fn()\n","        self.channel_select = nn.Linear(reduce_channels, in_channels)\n","        self.spatial_select = nn.Linear(reduce_channels * 2, 1)\n","        self.gate_fn = gate_fn()\n","\n","\n","    def forward(self, x):\n","        ori_x = x\n","\n","        x = self.norm(x)\n","        copy = x\n","        copy = copy.permute(0, 3, 1, 2)\n","        x_global = F.avg_pool2d(copy, x.shape[2], x.shape[3])\n","        x_global = x_global.permute(0, 2, 3, 1)\n","        x_global = self.act_fn(self.global_reduce(x_global))\n","        x_local = self.act_fn(self.local_reduce(x))\n","\n","        c_attn = self.channel_select(x_global)\n","        c_attn = self.gate_fn(c_attn)\n","        s_attn = self.spatial_select(torch.cat([x_local, x_global.repeat(1, x.shape[1], x.shape[2], 1)], dim=-1))\n","        s_attn = self.gate_fn(s_attn)\n","        attn = c_attn * s_attn\n","        return ori_x * attn\n"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":465,"status":"ok","timestamp":1721268850377,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"C-1TVkvDe5tA"},"outputs":[],"source":["class ConcactFeature(nn.Module):\n","    def __init__(self, dim=3):\n","        super(ConcactFeature, self).__init__()\n","        self.catConv = nn.Conv2d(3, 3, kernel_size=1)\n","        self.norm1 = nn.LayerNorm([3, patch_size, patch_size])\n","        self.conv = nn.Conv2d(3, 3, 1)\n","\n","    def forward(self, x):\n","        x = self.catConv(x)\n","        x = self.norm1(x)\n","        x = self.conv(x)\n","        return x\n","\n","\n","class WBANet(nn.Module):\n","    def __init__(self):\n","        super(WBANet, self).__init__()\n","        self.wsm = wsm\n","        self.bam = BiAttn(in_channels=3)\n","        self.cf = ConcactFeature()\n","        self.linear1 = nn.Linear(patch_size * patch_size * 3, 20)\n","        self.linear2 = nn.Linear(20, 2)\n","        self.drop = nn.Dropout(0.2)\n","        self.catConv = nn.Conv2d(6, 3, kernel_size=1)\n","\n","\n","\n","    def forward(self, img):\n","        wsmOut = self.wsm(img)\n","        bamOut = self.bam(img.permute(0, 2, 3, 1))\n","        catOut = torch.cat((wsmOut, bamOut), 3).permute(0, 3, 1, 2)\n","        catOut = self.catConv(catOut)\n","\n","        x = self.cf(catOut)\n","\n","        out1 = x.view(x.size(0), -1)  # 128 192\n","        out = self.linear1(out1)\n","        out = self.linear2(out)\n","        return out\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["060e8d89de414cb39c0596247b0822b5","f504692c0ed44d25983f8e85576fde14","1c2fef371eec47639bf617dd660aee6f","b3e9a0f8e3694e0f9b999b070bffed7e","fb41a0d9bfce4958a6f4050706a0fc4c","509158328be84e8a9c015c786f7e3dc7","226455e3d2174cba8b9febf51e881402","bef75e62fd9a447db5e369fc4060600c","a436b806cf7f4b3d8f58109dbd7efe86","908d68b8167c40eb88ec31abb652c598","01325e687d1f4de49fd9ffe69163350e","2b074fa25b3e49cab1102dcaa95f8dee","5ba30ed285134c69932118724a7b60d8","228fb97372bf419880e0135775edd3cb","b60c766c37cd4a1182c27ca66a7c4c31","ded014e3fe7e43a7b43cbd260e9d209b","9b8618ab79e3430cb2c5d0dd83f7c425","b894c2e4aa2f44d6af1ddea84241e0f9","c3c44e5ef0b44a3a87b8ed34c19d7bb7","2bd339db89574d258a6ccfb853b056e7","44ab9cc437854590ad92e18a67ab2ff3","e078f61054b14265b44cacc723e8cbc9","ce4d7a794cd247829a1f8d4bda3b95f2","cf1119de169d4cefb0c9fcf0eae2afb7","4d814482bc2b48ce9ac6c41aae38e19c","9ab1ef8fb5b84b3eb3c21825a87c2132","41a42b073d4a43d1840ffe56f2883b83","ba72ede0c24846319c0a0964cb42100f","69b2966078904d0d8eb756645a85d02b","2ea7cef1306b4543916f37973115110f","29ed5b47cceb4ef1bedcd05e8ecca731","4b02cd64a7b542edbfeb4cf6342373e6","64e913c5299a456ba477ff03af072c52","d54b7233c99c47838702bf8e2fac156c","3f48d645af584d5cafc8ed45faf6d58d","b179579ce03f4f31b06c6247a59f4a35","d6cfa1d3c5b34745bfef3d99568e2bd1","6a471e2264bb4b88a8f27b320ab91389","c066c67244e74711826059434f194cfd","dd4c8345aefe48a8bbeb1946ef2a2d4c","c2eeaf3db4ba41a98032f64ab1cb344e","fd833483e98a4e7f8a08162a4e52565c","740f4a9bf00845a8bb43e6716828aa6d","0c8219c431b94395807816404c568785","3f812e696920408691e03588bf7974ed","698893ade08c4b90adbc8ba723c25d76","0e17278527834bc6be8188280ecaeee8","5d32ab95134f47aaa5a04ec43966ef21","7a45f32e73264f0094db4414ffbd19a3","fc4729919a50470cb1a979cb7098516d","5c0397f489da46fc969157e9ba8242e0","6dbad70977db43d895902514c2ef44f1","1520b051bd154d0e8c0db94b912f25d7","0f55f090612b40ff8ac7151d68577571","d842798d6fad4174b4739bdcd2b9d734","3b8d902fc79744909a69312738ba2d34","45a12a72ffa84320ad7c4c2e92dce17e","e50d6c83e4c84264bd858cefef8a3c67","9e7c9c6946cb4e31b928f5f6de12e9b5","029616a2463c49e7b02048d6e354a261","9d503651899642819fb40a411ac58238","0275e65987554c798f463dd1290f5a24","d0d62fce88fe41eb82da78ef3c3bf187","581f448fe8b74d2abae1732da017a1e7","3693d992fd86435aace1ad89a0166c48","8531530583a1491d81c156f6e52cff2e","61642ab75b4b40d4903070f43eadb75c","a11df2dfb157452d8134aca4c213eccf","041433fa5f0642b9912050ac814b4ac5","0de80a9470d74790b7ebf5ccc8be2a8c","e640af45d7944ccabde84312eea28748","215bde34a26d4d52b581e0a2780a4fd2","b5b206e1023447c395d28cd093545a14","2bde418857b54f0c86c047f45fde07fc","128eed6954a148c6ac5bde0d0ccc4ac0","fbf92b07d5074be59aac9ea2bef4d736","dd42fb49c8af40f5a7463f3f0d9bee39","f25ad108a4884e36a7e981761df33ca2","b256c48f115643008ceb3e916b0e8d1b","76fb268e22b84079a0f81951d791acf3","3652b09c8cfe45b7bfb1f8c2c30890a2","242cfbbf15fc405c9e89eec4398364be","a48a01b935994c59b4360c2177216630","a9d4cc216afd4823803dd3dfcffd4a86","92bec0f04f2b4cd4a2247ef9139aacbe","b5fd0763980c4138bfd64ab05bb94844","80398c819e544951bbd1b68f7924b2df","77e1c4c35b764bb4a9a53170a1728cfb","a15dc4f044de4b6c98a991285e737a7b","0b88a21aaca44acca0a6629bee820cb1","81169b6b0d0e468baf4ea3af7118af27","feae58bd69bf4c82b0c11698039ce7cc","dd14843838624433aa6b91f41513ccf2","e1696b446104428cb00f70f2585cecb9","968fb022f9cb4b5299e8cf4f4c410ee9","308b7dbcca4d43fa968b3da0219c3f3e","e421e269c06f41b581b5131b0069c3fb","307f49e5f66e4103afc2df7e4ba28449","725a877a2ce447719370ffa536a95f3f","0cfe8926eda2415a9a2229382984759c","32a2827deec5436ca0c50b00ce6f0b7a","f079f9bcd74e431db37be6b39e2602d1","a0fc8fed285340f29051ec89a91c3385","31156f863c12465298c346432c775dae","b07f7b302fe54d5d828016f9f9250c94","f87c5cfd4af9421ab0dcbe6712cb4f5f","238458cd0d7444fcae6d40c58eacb16e","3aba499ed16c4bf7bc39d15f4044f099","c2baf6909fff40be850d56f9ccdc8849","4c34959c1e65411695edec439843f568","2a8f82230627464ab02fda635470c93c","ad55328e18b44ee894581d4136cba913","9c591d90fcaa40f982a236dd3dbc5262","0e74eeebb3bf49bb831d84f6c6626884","df962574f700439f97016d3fbe40348f","2c2facc07f8d4693abab36cf212deecf","71a93b5f0cd74568a76d9da594e6a80e","d9de2ca166654ddcb18570f311703179","f04bac790b7c464d9b3619c22e817f69","6aa878476e5846e1bec6adb8952787b5","b8a10af138b6403793a918933e12833c","07b4e1a57af7459dbb5d5de4b085f989","d29bcb4174c9498da5a3b35e1a21dd65","fc697a0f292f4a81ad583a061d2fa601","f4384a717ff64c44a9930df5e53d8977","a9dc8e9e055546a79bfc4773196a00b9","2c2037532ec84ee2ab6cf2aef2349ba9","ec04d0627c1a4c338e781150dc069837","0347c607915247769b43803f809f294b","6cdab06dcadf4f139f43b1cc33b8c9b3","5248bcba909343c49e2986c47928d086","f8e49007cbb04e589b58eead8a445999","4d481bf3e8f3427e9da8ee2397a998e6","b4968c0178cc4d4d9bfe8b9192f8ae06","c57af4edb9e049849863a30119b2d088","24df0e46beb24cd39dc10e47248d40c8","67a9348a2ec64dd9a3d58d6e66f9fe32","85e3c9ba4b00426390ea901b1634a96b","82b2d2d2bfa94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= 0.75\n","criterion = nn.CrossEntropyLoss()\n","model = WBANet().to(device)\n","lr = 5e-3\n","epoch = 10\n","optimizer = optim.Adam(model.parameters(), lr=lr)\n","scheduler = StepLR(optimizer, step_size=1, gamma=gamma)\n","start_time = time.time()\n","for epoch in range(epoch):\n","    epoch_loss = 0\n","    epoch_accuracy = 0\n","    for data, label in tqdm(train_loader):\n","        data = data.to(device)\n","        label = label.to(device)\n","        output = model(data)\n","        loss = criterion(output, label)\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()\n","\n","        acc = (output.argmax(dim=1) == label).float().mean()\n","        epoch_accuracy += acc / len(train_loader)\n","        epoch_loss += loss / len(train_loader)\n","\n","    print(f\"Epoch : {epoch + 1} - loss : {epoch_loss:.4f} - acc: {epoch_accuracy:.4f}\\n\")\n","\n","\n","\n","end_time = time.time()\n","\n","\n","execution_time = end_time - start_time\n","\n","\n","print(f\"train run time: {execution_time:.4f} seconds\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":783},"collapsed":true,"executionInfo":{"elapsed":447428,"status":"ok","timestamp":1721270149151,"user":{"displayName":"Jiangwei Xie","userId":"03535566663870232640"},"user_tz":-480},"id":"XvXCFtCmhScB","outputId":"a8fdeb3c-dff4-4573-8d20-1ad571dd2293"},"outputs":[],"source":["\n","model.eval()\n","outputs = np.zeros((ylen, xlen))\n","start_time = time.time()\n","for i in range(ylen):\n","    for j in range(xlen):\n","        if preclassify_lab[i, j] != 1.5:\n","            outputs[i, j] = preclassify_lab[i, j]\n","        else:\n","            img_patch = x_test[i * xlen + j, :, :, :]\n","            img_patch = img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])\n","            img_patch = torch.FloatTensor(img_patch).to(device)\n","            prediction = model(img_patch)\n","            prediction = np.argmax(prediction.detach().cpu().numpy(), axis=1)\n","            outputs[i, j] = prediction + 1\n","\n","    if (i + 1) % 50 == 0:\n","        print('... ... row', i + 1, ' handling ... ...')\n","\n","\n","outputs = outputs - 1\n","res = outputs * 255\n","res = postprocess(res)\n","evaluate(im_gt, res)\n","plt.imshow(res, 'gray')\n","plt.axis('off')  # remove coordinate axis\n","plt.xticks([])  # remove x axis\n","plt.yticks([])  # remove y axis\n","\n","plt.show()\n","\n","end_time = time.time()\n","\n","execution_time = end_time - start_time\n","\n","print(f\"test run time: {execution_time:.4f} seconds\")\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gwDWgu5Tfs8S"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 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